There is an ever-increasing need to recognize text in an image in applications such as video surveillance. These applications require a device that can sense and capture an image. For example, a charge-coupled device (CCD) is included in many imaging devices, such as cell phones and security cameras. However, several problems may occur due to low resolution CCD devices, since text can become degraded by blurring due to the distance of the document from the imaging device, poor resolution due to insufficient sensor outputs from the imaging device, and uncorrelated noise from a variety of sources, including, but not limited to, noise due to sensor behavior in low light environments. These degradations may be so great as to render the text contained within the image illegible.
Existing conventional methods are available for the restoration of text images. However, the conventional methods have several disadvantages. One such method includes subspace identification, for example, disclosed in D. Rajan and S. Chaudhuri, “Simultaneous estimation of superresolved scene and depth map from low resolution defocused observations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1102-17, September, 2003, and G. B. Giannakis, R. W. Heath, Jr, “Blind Identification of Multichannel FIR Blurs and Perfect Image Restoration,” IEEE Transactions on Image Processing, vol. 9 no. 11 pp 1877-96, November, 2000, each incorporated herein by reference in its entirety, which characterizes blurring and recovers an original scene by exploiting multiple observations of the same scene. However, this approach is too slow for bulk processing. In addition, this approach requires multiple observations of the same scene, and is not usually available.
Another method includes clustering algorithms, for example, disclosed in M. Ozdil and F. Vural, “Optical character recognition without segmentation,” Document Analysis and Recognition, 1997, Proceedings of the Fourth International Conference on, vol. 2, 18-20 Aug. 1997, pp. 483-486 vol. 2., incorporated herein by reference in its entirety, which creates an estimate of an original scene by averaging similar regions of an input image. However, this method is prone to clustering dissimilar regions, thus producing spurious estimates.
Another method includes alternating minimization based on statistical regularization, for example, disclosed in R. Schultz and R. Stevenson, “A baysian approach to image expansion for improved definition,” IEEE Trans. Image Processing, vol. 3, no. 3, pp. 233-242, May 1994, G. Ayers and J. Dainty, “Iterative blind deconvolution method and its applications,” Optical Letters, vol. 13, pp. 547-, 1988, P. D. Thouin and C. I. Chang, “A method for restoration of low resolution document images,” International Journal on Document Analysis and Recognition, no. 2, pp. 200-210, 2000, and Y. L. You and M. Kaveh, “A regularization approach to joint blur identification and image restoration,” IEEE Trans. Image Processing, vol. 5, pp. 416-28, March 1996, each incorporated herein by reference it its entirety. Alternating minimization based on statistical regularization exploits prior statistical information about the desired enhanced estimate. However, the assumed models on desired solutions are true in a limiting sense, and these models tend to be inadequate for small data sets such as single images.
Another method includes blind equalization techniques, for example, disclosed in D. Kundur, “Blind Deconvolution of Still Images using Recursive Inverse Filtering,” Master's Thesis, University of Toronto, 1995, incorporated herein by reference in its entirety, which works by choosing a solution closest to some deterministic, not statistical, property of the desired solution. However, this method requires information that usually not available, and is too slow for bulk processing.
In addition, the conventional methods described herein are focused on low resolution scenarios geared towards improving machine readability (COR), and do not address blurring restoration.
Thus, there is a requirement for a system and method that provides a computationally inexpensive and statistically robust reconstruction of original text data without having to rely on information which may be imperfectly known or altogether unavailable to the system operator.